Cost-effective data analytics across multiple cloud regions

Junyi Shu, Xin Jin, Yun Ma, Xuanzhe Liu, Gang Huang
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Abstract

We propose a cloud-native data analytics engine for processing data stored among geographically distributed cloud regions with reduced cost. A job is split into subtasks and placed across regions based on factors including prices of compute resources and data transmission. We present its architecture which leverages existing cloud infrastructures and discuss major challenges of its system design. Preliminary experiments show that the cost is reduced by 15.1% for a decision support query on a four-region public cloud setup.
跨多个云区域的经济高效的数据分析
我们提出了一种云原生数据分析引擎,用于以更低的成本处理存储在地理分布云区域之间的数据。根据计算资源价格和数据传输等因素,将作业拆分为子任务并跨区域放置。我们介绍了其利用现有云基础设施的架构,并讨论了其系统设计的主要挑战。初步实验表明,在四区域公共云设置上,决策支持查询的成本降低了15.1%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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